Bayesian preference elicitation for decision support in multiobjective optimization

• URL: http://arxiv.org/abs/2507.16999v1
• Date: Tue, 22 Jul 2025 20:14:20 GMT
• Title: Bayesian preference elicitation for decision support in multiobjective optimization
• Authors: Felix Huber, Sebastian Rojas Gonzalez, Raul Astudillo,
• Abstract summary: We use a Bayesian model to estimate the decision-maker's utility function based on pairwise comparisons.<n>A principled elicitation strategy selects queries interactively to balance exploration and exploitation.<n>At the end of the elicitation phase, it generates a reduced menu of high-quality solutions.
• Score: 7.332652485849632
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesian model to estimate the decision-maker's utility function based on pairwise comparisons. Aided by this model, a principled elicitation strategy selects queries interactively to balance exploration and exploitation, guiding the discovery of high-utility solutions. The approach is flexible: it can be used interactively or a posteriori after estimating the Pareto front through standard multi-objective optimization techniques. Additionally, at the end of the elicitation phase, it generates a reduced menu of high-quality solutions, simplifying the decision-making process. Through experiments on test problems with up to nine objectives, our method demonstrates superior performance in finding high-utility solutions with a small number of queries. We also provide an open-source implementation of our method to support its adoption by the broader community.

Related papers

• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance [41.24995294376129]
  Multi-objective learning under user-specified preference is common in real-world problems such as multi-lingual speech recognition under fairness.<n>We frame such a problem as a semivectorial bilevel optimization problem, whose goal is to optimize a pre-defined preference function.<n>We propose algorithms to solve the reformulated single-level problem, and establish its convergence guarantees.
  arXiv  Detail & Related papers  (2025-03-26T16:41:07Z)
• Preference-Guided Diffusion for Multi-Objective Offline Optimization [64.08326521234228]
  We propose a preference-guided diffusion model for offline multi-objective optimization.<n>Our guidance is a preference model trained to predict the probability that one design dominates another.<n>Our results highlight the effectiveness of classifier-guided diffusion models in generating diverse and high-quality solutions.
  arXiv  Detail & Related papers  (2025-03-21T16:49:38Z)
• Learning Joint Models of Prediction and Optimization [56.04498536842065]
  Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
  arXiv  Detail & Related papers  (2024-09-07T19:52:14Z)
• Differentiation of Multi-objective Data-driven Decision Pipeline [34.577809430781144]
  Real-world scenarios frequently involve multi-objective data-driven optimization problems. Traditional two-stage methods apply a machine learning model to estimate problem coefficients, followed by invoking a solver to tackle the predicted optimization problem. Recent efforts have focused on end-to-end training of predictive models that use decision loss derived from the downstream optimization problem.
  arXiv  Detail & Related papers  (2024-06-02T15:42:03Z)
• UCB-driven Utility Function Search for Multi-objective Reinforcement Learning [51.00436121587591]
  In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours.<n>We focus on the case of linear utility functions parametrised by weight vectors w.<n>We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process.
  arXiv  Detail & Related papers  (2024-05-01T09:34:42Z)
• Data-Efficient Interactive Multi-Objective Optimization Using ParEGO [6.042269506496206]
  Multi-objective optimization seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives. In practical applications, decision-makers (DMs) will select a single solution that aligns with their preferences to be implemented. We propose two novel algorithms that efficiently locate the most preferred region of the Pareto front in expensive-to-evaluate problems.
  arXiv  Detail & Related papers  (2024-01-12T15:55:51Z)
• Multi-Objective Bayesian Optimization with Active Preference Learning [18.066263838953223]
  We propose a Bayesian optimization (BO) approach to identifying the most preferred solution in a multi-objective optimization (MOO) problem. To minimize the interaction cost with the decision maker (DM), we also propose an active learning strategy for the preference estimation.
  arXiv  Detail & Related papers  (2023-11-22T15:24:36Z)
• Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization [59.386153202037086]
  Predict-Then- framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This approach can be inefficient and requires handcrafted, problem-specific rules for backpropagation through the optimization step. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
  arXiv  Detail & Related papers  (2023-11-22T01:32:06Z)
• Learning Proximal Operators to Discover Multiple Optima [66.98045013486794]
  We present an end-to-end method to learn the proximal operator across non-family problems. We show that for weakly-ized objectives and under mild conditions, the method converges globally.
  arXiv  Detail & Related papers  (2022-01-28T05:53:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.